Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations152
Missing cells410
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.0 KiB
Average record size in memory208.9 B

Variable types

Numeric19
Categorical3
DateTime4

Alerts

centr_2_turb_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
dur_cf is highly overall correlated with orden_encadenado_cfHigh correlation
dur_ino is highly overall correlated with orden_encadenado_cfHigh correlation
id_bio_y is highly overall correlated with lote and 2 other fieldsHigh correlation
id_centr is highly overall correlated with loteHigh correlation
lote is highly overall correlated with id_bio_y and 3 other fieldsHigh correlation
lote_parental_cf is highly overall correlated with id_bio_y and 4 other fieldsHigh correlation
orden_encadenado_cf is highly overall correlated with dur_cf and 9 other fieldsHigh correlation
producto_1_cf is highly overall correlated with producto_2_cfHigh correlation
producto_2_cf is highly overall correlated with producto_1_cfHigh correlation
turb_diff_ino is highly overall correlated with orden_encadenado_cf and 1 other fieldsHigh correlation
turb_fin_cultivo is highly overall correlated with orden_encadenado_cf and 1 other fieldsHigh correlation
turb_fin_cultivo_cf is highly overall correlated with centr_2_turb_cf and 1 other fieldsHigh correlation
turb_inicio_cultivo is highly overall correlated with orden_encadenado_cfHigh correlation
turb_inicio_cultivo_cf is highly overall correlated with lote_parental_cfHigh correlation
turbidez_diff_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
viab_fin_cultivo_ino is highly overall correlated with orden_encadenado_cfHigh correlation
vol_cultivo_ino is highly overall correlated with orden_encadenado_cfHigh correlation
vol_ino_util_cf is highly overall correlated with lote_parental_cfHigh correlation
orden_encadenado_cf is highly imbalanced (55.1%)Imbalance
lote_parental_cf has 130 (85.5%) missing valuesMissing
vol_ino_util_cf has 5 (3.3%) missing valuesMissing
centr_1_turb_cf has 4 (2.6%) missing valuesMissing
centr_2_turb_cf has 9 (5.9%) missing valuesMissing
id_bio_y has 26 (17.1%) missing valuesMissing
f_h_inicio_ino has 30 (19.7%) missing valuesMissing
f_h_fin_ino has 30 (19.7%) missing valuesMissing
vol_cultivo_ino has 30 (19.7%) missing valuesMissing
turb_inicio_cultivo has 32 (21.1%) missing valuesMissing
turb_fin_cultivo has 26 (17.1%) missing valuesMissing
viab_fin_cultivo_ino has 26 (17.1%) missing valuesMissing
turb_diff_ino has 32 (21.1%) missing valuesMissing
dur_ino has 30 (19.7%) missing valuesMissing
lote has unique valuesUnique

Reproduction

Analysis started2024-10-13 15:46:26.854547
Analysis finished2024-10-13 15:46:54.244892
Duration27.39 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

lote
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct152
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23323.151
Minimum23019
Maximum24053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:54.295996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23019
5-th percentile23026.55
Q123060.75
median23101.5
Q324003.25
95-th percentile24044.45
Maximum24053
Range1034
Interquartile range (IQR)942.5

Descriptive statistics

Standard deviation416.71493
Coefficient of variation (CV)0.017867008
Kurtosis-0.75084625
Mean23323.151
Median Absolute Deviation (MAD)43
Skewness1.1084266
Sum3545119
Variance173651.33
MonotonicityNot monotonic
2024-10-13T17:46:54.369587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23019 1
 
0.7%
23131 1
 
0.7%
23123 1
 
0.7%
23124 1
 
0.7%
23125 1
 
0.7%
23126 1
 
0.7%
23127 1
 
0.7%
23129 1
 
0.7%
23130 1
 
0.7%
23132 1
 
0.7%
Other values (142) 142
93.4%
ValueCountFrequency (%)
23019 1
0.7%
23020 1
0.7%
23021 1
0.7%
23022 1
0.7%
23023 1
0.7%
23024 1
0.7%
23025 1
0.7%
23026 1
0.7%
23027 1
0.7%
23028 1
0.7%
ValueCountFrequency (%)
24053 1
0.7%
24052 1
0.7%
24051 1
0.7%
24050 1
0.7%
24049 1
0.7%
24047 1
0.7%
24046 1
0.7%
24045 1
0.7%
24044 1
0.7%
24043 1
0.7%

orden_encadenado_cf
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
127 
2
23 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters152
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Length

2024-10-13T17:46:54.434616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:46:54.487050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

lote_parental_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing130
Missing (%)85.5%
Infinite0
Infinite (%)0.0%
Mean23571.818
Minimum23085
Maximum24051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:54.539078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23085
5-th percentile23099.05
Q123112.25
median23567.5
Q324034.75
95-th percentile24049.7
Maximum24051
Range966
Interquartile range (IQR)922.5

Descriptive statistics

Standard deviation472.6577
Coefficient of variation (CV)0.020051813
Kurtosis-2.2071206
Mean23571.818
Median Absolute Deviation (MAD)459.5
Skewness0.00017902579
Sum518580
Variance223405.3
MonotonicityNot monotonic
2024-10-13T17:46:54.597888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24010 1
 
0.7%
24050 1
 
0.7%
24044 1
 
0.7%
24041 1
 
0.7%
24036 1
 
0.7%
24037 1
 
0.7%
24031 1
 
0.7%
24027 1
 
0.7%
24021 1
 
0.7%
24020 1
 
0.7%
Other values (12) 12
 
7.9%
(Missing) 130
85.5%
ValueCountFrequency (%)
23085 1
0.7%
23099 1
0.7%
23100 1
0.7%
23108 1
0.7%
23109 1
0.7%
23112 1
0.7%
23113 1
0.7%
23118 1
0.7%
23119 1
0.7%
23124 1
0.7%
ValueCountFrequency (%)
24051 1
0.7%
24050 1
0.7%
24044 1
0.7%
24041 1
0.7%
24037 1
0.7%
24036 1
0.7%
24031 1
0.7%
24027 1
0.7%
24021 1
0.7%
24020 1
0.7%

id_bio_x
Real number (ℝ)

Distinct7
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14120.803
Minimum13169
Maximum14617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:54.646636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13169
5-th percentile13169
Q113170
median14614
Q314616
95-th percentile14617
Maximum14617
Range1448
Interquartile range (IQR)1446

Descriptive statistics

Standard deviation687.94411
Coefficient of variation (CV)0.048718485
Kurtosis-1.5687935
Mean14120.803
Median Absolute Deviation (MAD)2
Skewness-0.67230589
Sum2146362
Variance473267.1
MonotonicityNot monotonic
2024-10-13T17:46:54.699064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14616 34
22.4%
14615 30
19.7%
13170 29
19.1%
14614 27
17.8%
13169 22
14.5%
14617 9
 
5.9%
13189 1
 
0.7%
ValueCountFrequency (%)
13169 22
14.5%
13170 29
19.1%
13189 1
 
0.7%
14614 27
17.8%
14615 30
19.7%
14616 34
22.4%
14617 9
 
5.9%
ValueCountFrequency (%)
14617 9
 
5.9%
14616 34
22.4%
14615 30
19.7%
14614 27
17.8%
13189 1
 
0.7%
13170 29
19.1%
13169 22
14.5%
Distinct103
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-21 06:30:00+00:00
Maximum2024-03-25 12:28:00+00:00
2024-10-13T17:46:54.765263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:54.839605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct137
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-23 05:30:00+00:00
Maximum2024-03-27 07:51:00+00:00
2024-10-13T17:46:54.916363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:54.990102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vol_ino_util_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)19.7%
Missing5
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean81.458503
Minimum66.4
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:55.056848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum66.4
5-th percentile79.44
Q180
median81.6
Q382.8
95-th percentile84.16
Maximum88
Range21.6
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.248108
Coefficient of variation (CV)0.027598199
Kurtosis13.302453
Mean81.458503
Median Absolute Deviation (MAD)1.6
Skewness-1.7398503
Sum11974.4
Variance5.0539895
MonotonicityNot monotonic
2024-10-13T17:46:55.114408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
80 44
28.9%
82.4 12
 
7.9%
83.2 12
 
7.9%
81.6 11
 
7.2%
82 11
 
7.2%
80.8 7
 
4.6%
84 7
 
4.6%
83.6 6
 
3.9%
81.2 6
 
3.9%
80.4 5
 
3.3%
Other values (19) 26
17.1%
ValueCountFrequency (%)
66.4 1
 
0.7%
76 1
 
0.7%
77.2 1
 
0.7%
77.6 2
 
1.3%
78.4 1
 
0.7%
78.8 1
 
0.7%
79.2 1
 
0.7%
80 44
28.9%
80.4 5
 
3.3%
80.56 1
 
0.7%
ValueCountFrequency (%)
88 1
 
0.7%
87.2 1
 
0.7%
86.4 1
 
0.7%
85.92 1
 
0.7%
85.6 1
 
0.7%
85.2 1
 
0.7%
84.16 3
 
2.0%
84 7
4.6%
83.6 6
3.9%
83.2 12
7.9%

turb_inicio_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.036316
Minimum12.56
Maximum44.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:55.174130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.56
5-th percentile14.8
Q116.4
median17.76
Q318.8
95-th percentile21.796
Maximum44.4
Range31.84
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation3.3008587
Coefficient of variation (CV)0.18301181
Kurtosis28.581161
Mean18.036316
Median Absolute Deviation (MAD)1.16
Skewness4.248607
Sum2741.52
Variance10.895668
MonotonicityNot monotonic
2024-10-13T17:46:55.245360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.84 6
 
3.9%
16.16 5
 
3.3%
16.64 5
 
3.3%
18.32 5
 
3.3%
17.76 5
 
3.3%
18 4
 
2.6%
18.72 4
 
2.6%
15.28 4
 
2.6%
17.6 4
 
2.6%
17.12 4
 
2.6%
Other values (62) 106
69.7%
ValueCountFrequency (%)
12.56 1
0.7%
13.36 1
0.7%
14.08 1
0.7%
14.4 1
0.7%
14.48 1
0.7%
14.56 2
1.3%
14.8 2
1.3%
14.88 2
1.3%
14.96 1
0.7%
15.04 1
0.7%
ValueCountFrequency (%)
44.4 1
0.7%
30.32 2
1.3%
27.04 1
0.7%
26.24 1
0.7%
23.2 1
0.7%
22 1
0.7%
21.84 1
0.7%
21.76 1
0.7%
21.44 1
0.7%
20.8 2
1.3%

turb_fin_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.416316
Minimum42.8
Maximum91.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:55.319715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42.8
5-th percentile59.828
Q169.1
median74.32
Q381.08
95-th percentile87.2
Maximum91.2
Range48.4
Interquartile range (IQR)11.98

Descriptive statistics

Standard deviation8.9408989
Coefficient of variation (CV)0.12014702
Kurtosis1.2364086
Mean74.416316
Median Absolute Deviation (MAD)5.64
Skewness-0.75227869
Sum11311.28
Variance79.939674
MonotonicityNot monotonic
2024-10-13T17:46:55.390296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 7
 
4.6%
83.2 6
 
3.9%
81.6 5
 
3.3%
80.8 4
 
2.6%
85.6 4
 
2.6%
74.4 3
 
2.0%
87.2 3
 
2.0%
69.04 3
 
2.0%
72.48 3
 
2.0%
73.52 2
 
1.3%
Other values (96) 112
73.7%
ValueCountFrequency (%)
42.8 1
0.7%
44.32 1
0.7%
49.36 1
0.7%
49.76 1
0.7%
54.16 1
0.7%
56.48 1
0.7%
56.96 1
0.7%
59.52 1
0.7%
60.08 1
0.7%
60.72 1
0.7%
ValueCountFrequency (%)
91.2 2
 
1.3%
90.4 2
 
1.3%
89.6 1
 
0.7%
88 1
 
0.7%
87.2 3
2.0%
86.4 2
 
1.3%
85.6 4
2.6%
84.8 2
 
1.3%
84 7
4.6%
83.2 6
3.9%

viab_final_cultivo_cf
Real number (ℝ)

Distinct101
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016579 × 108
Minimum70400000
Maximum3.696 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:55.456777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum70400000
5-th percentile1.1448 × 108
Q11.48 × 108
median1.652 × 108
Q31.922 × 108
95-th percentile2.2828 × 108
Maximum3.696 × 108
Range2.992 × 108
Interquartile range (IQR)44200000

Descriptive statistics

Standard deviation38308279
Coefficient of variation (CV)0.22512327
Kurtosis4.5153204
Mean1.7016579 × 108
Median Absolute Deviation (MAD)20400000
Skewness0.99890815
Sum2.58652 × 1010
Variance1.4675242 × 1015
MonotonicityNot monotonic
2024-10-13T17:46:55.528426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195200000 5
 
3.3%
164000000 4
 
2.6%
145600000 4
 
2.6%
185600000 3
 
2.0%
157600000 3
 
2.0%
158400000 3
 
2.0%
163200000 3
 
2.0%
153600000 3
 
2.0%
156800000 3
 
2.0%
184000000 3
 
2.0%
Other values (91) 118
77.6%
ValueCountFrequency (%)
70400000 1
0.7%
91200000 1
0.7%
95200000 1
0.7%
97600000 1
0.7%
100000000 1
0.7%
101600000 1
0.7%
104000000 1
0.7%
113600000 1
0.7%
115200000 1
0.7%
117600000 1
0.7%
ValueCountFrequency (%)
369600000 1
0.7%
280000000 1
0.7%
262400000 1
0.7%
260000000 1
0.7%
248000000 1
0.7%
240000000 1
0.7%
232000000 1
0.7%
229600000 1
0.7%
227200000 1
0.7%
224000000 1
0.7%

id_centr
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
14246
60 
17825
54 
12912
36 
6379
 
2

Length

Max length5
Median length5
Mean length4.9868421
Min length4

Characters and Unicode

Total characters758
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17825
2nd row14246
3rd row17825
4th row12912
5th row17825

Common Values

ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Length

2024-10-13T17:46:55.598909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:46:55.656495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

centr_1_turb_cf
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)62.2%
Missing4
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean30.067703
Minimum21.28
Maximum168.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:55.719077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21.28
5-th percentile23.096
Q126.44
median28.56
Q330.5
95-th percentile33.44
Maximum168.8
Range147.52
Interquartile range (IQR)4.06

Descriptive statistics

Standard deviation15.167552
Coefficient of variation (CV)0.50444664
Kurtosis67.864839
Mean30.067703
Median Absolute Deviation (MAD)2.04
Skewness8.063087
Sum4450.02
Variance230.05462
MonotonicityNot monotonic
2024-10-13T17:46:55.790497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.84 7
 
4.6%
28.72 5
 
3.3%
29.44 4
 
2.6%
28.56 4
 
2.6%
30.4 4
 
2.6%
26.56 4
 
2.6%
27.44 3
 
2.0%
30.16 3
 
2.0%
31.76 3
 
2.0%
29.52 3
 
2.0%
Other values (82) 108
71.1%
(Missing) 4
 
2.6%
ValueCountFrequency (%)
21.28 1
0.7%
21.52 1
0.7%
21.76 1
0.7%
21.84 1
0.7%
22.08 1
0.7%
22.4 1
0.7%
22.64 1
0.7%
23.04 1
0.7%
23.2 1
0.7%
23.28 1
0.7%
ValueCountFrequency (%)
168.8 1
 
0.7%
142.4 1
 
0.7%
40.9 1
 
0.7%
36.64 1
 
0.7%
34.48 1
 
0.7%
34 1
 
0.7%
33.6 1
 
0.7%
33.44 3
2.0%
33.2 1
 
0.7%
32.72 1
 
0.7%

centr_2_turb_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)78.3%
Missing9
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean23.56979
Minimum9.84
Maximum156.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:55.975218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.84
5-th percentile12.2
Q117.72
median20.72
Q325
95-th percentile37.704
Maximum156.96
Range147.12
Interquartile range (IQR)7.28

Descriptive statistics

Standard deviation17.21646
Coefficient of variation (CV)0.73044604
Kurtosis46.177047
Mean23.56979
Median Absolute Deviation (MAD)3.84
Skewness6.3247727
Sum3370.48
Variance296.4065
MonotonicityNot monotonic
2024-10-13T17:46:56.043558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
2.6%
21.36 4
 
2.6%
20.8 4
 
2.6%
17.76 3
 
2.0%
19.52 3
 
2.0%
20.88 2
 
1.3%
17.84 2
 
1.3%
22.24 2
 
1.3%
15.36 2
 
1.3%
20.72 2
 
1.3%
Other values (102) 115
75.7%
(Missing) 9
 
5.9%
ValueCountFrequency (%)
9.84 1
0.7%
10.08 1
0.7%
10.4 2
1.3%
11.44 1
0.7%
11.6 2
1.3%
12.16 1
0.7%
12.56 1
0.7%
12.88 1
0.7%
13.2 1
0.7%
13.36 1
0.7%
ValueCountFrequency (%)
156.96 1
0.7%
151.76 1
0.7%
54.8 1
0.7%
49.04 1
0.7%
44.4 1
0.7%
44 1
0.7%
38.4 1
0.7%
37.84 1
0.7%
36.48 1
0.7%
34.48 1
0.7%

producto_1_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.3157
Minimum526.4
Maximum2395.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:56.113580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum526.4
5-th percentile1175.112
Q11466.76
median1675.4
Q31853.798
95-th percentile2140.896
Maximum2395.36
Range1868.96
Interquartile range (IQR)387.038

Descriptive statistics

Standard deviation307.71306
Coefficient of variation (CV)0.18555758
Kurtosis0.45810105
Mean1658.3157
Median Absolute Deviation (MAD)197.08
Skewness-0.34206353
Sum252063.99
Variance94687.327
MonotonicityNot monotonic
2024-10-13T17:46:56.186907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1468.88 2
 
1.3%
1517.92 2
 
1.3%
1747.92 1
 
0.7%
1902.96 1
 
0.7%
1978.16 1
 
0.7%
2117.76 1
 
0.7%
1688.08 1
 
0.7%
2395.36 1
 
0.7%
2155.76 1
 
0.7%
1116.64 1
 
0.7%
Other values (140) 140
92.1%
ValueCountFrequency (%)
526.4 1
0.7%
969.888 1
0.7%
970.8 1
0.7%
988.096 1
0.7%
1096.584 1
0.7%
1101.04 1
0.7%
1116.64 1
0.7%
1151.44 1
0.7%
1194.48 1
0.7%
1198.16 1
0.7%
ValueCountFrequency (%)
2395.36 1
0.7%
2338.56 1
0.7%
2263.2 1
0.7%
2162.48 1
0.7%
2161.12 1
0.7%
2155.76 1
0.7%
2151.536 1
0.7%
2150.576 1
0.7%
2132.976 1
0.7%
2129.92 1
0.7%

producto_2_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1209789
Minimum2.8
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:56.257167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.936
Q15.1
median6.08
Q37.12
95-th percentile8.312
Maximum9.2
Range6.4
Interquartile range (IQR)2.02

Descriptive statistics

Standard deviation1.4079732
Coefficient of variation (CV)0.23002418
Kurtosis-0.57667228
Mean6.1209789
Median Absolute Deviation (MAD)1.04
Skewness-0.0097291881
Sum930.3888
Variance1.9823884
MonotonicityNot monotonic
2024-10-13T17:46:56.324895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.16 6
 
3.9%
6.88 5
 
3.3%
6.56 5
 
3.3%
5.44 5
 
3.3%
5.52 5
 
3.3%
4.48 5
 
3.3%
5.76 4
 
2.6%
6.72 4
 
2.6%
5.28 4
 
2.6%
4.88 4
 
2.6%
Other values (54) 105
69.1%
ValueCountFrequency (%)
2.8 1
 
0.7%
2.96 1
 
0.7%
3.04 1
 
0.7%
3.44 2
1.3%
3.6 1
 
0.7%
3.68 1
 
0.7%
3.76 1
 
0.7%
4.08 2
1.3%
4.16 2
1.3%
4.24 3
2.0%
ValueCountFrequency (%)
9.2 1
 
0.7%
9.12 1
 
0.7%
8.96 1
 
0.7%
8.72 2
1.3%
8.64 1
 
0.7%
8.48 1
 
0.7%
8.4 1
 
0.7%
8.24 1
 
0.7%
8.16 2
1.3%
8.08 4
2.6%

dur_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173236.18
Minimum151200
Maximum193500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:56.396732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum151200
5-th percentile159600
Q1171045
median174150
Q3177300
95-th percentile182700
Maximum193500
Range42300
Interquartile range (IQR)6255

Descriptive statistics

Standard deviation7021.1053
Coefficient of variation (CV)0.040529092
Kurtosis0.60961578
Mean173236.18
Median Absolute Deviation (MAD)3150
Skewness-0.53540339
Sum26331900
Variance49295919
MonotonicityNot monotonic
2024-10-13T17:46:56.468788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172800 15
 
9.9%
176400 11
 
7.2%
178200 9
 
5.9%
174600 7
 
4.6%
169200 6
 
3.9%
171900 5
 
3.3%
180900 4
 
2.6%
171300 4
 
2.6%
175200 4
 
2.6%
162000 4
 
2.6%
Other values (57) 83
54.6%
ValueCountFrequency (%)
151200 1
 
0.7%
156180 1
 
0.7%
157200 1
 
0.7%
157500 1
 
0.7%
158400 2
1.3%
159300 1
 
0.7%
159600 3
2.0%
161100 2
1.3%
162000 4
2.6%
162300 1
 
0.7%
ValueCountFrequency (%)
193500 1
 
0.7%
189000 1
 
0.7%
187200 1
 
0.7%
185700 1
 
0.7%
185400 1
 
0.7%
183300 1
 
0.7%
182700 3
2.0%
181980 1
 
0.7%
181800 3
2.0%
181500 1
 
0.7%

turbidez_diff_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.38
Minimum24.72
Maximum73.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:56.536150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.72
5-th percentile41.688
Q151.04
median56.32
Q363.6
95-th percentile69.592
Maximum73.92
Range49.2
Interquartile range (IQR)12.56

Descriptive statistics

Standard deviation9.1640079
Coefficient of variation (CV)0.16254005
Kurtosis0.77084379
Mean56.38
Median Absolute Deviation (MAD)6.6
Skewness-0.62096502
Sum8569.76
Variance83.979041
MonotonicityNot monotonic
2024-10-13T17:46:56.604877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.48 3
 
2.0%
49.52 3
 
2.0%
65.2 2
 
1.3%
49.36 2
 
1.3%
48.08 2
 
1.3%
52.8 2
 
1.3%
68.16 2
 
1.3%
44.72 2
 
1.3%
54.16 2
 
1.3%
62.96 2
 
1.3%
Other values (128) 130
85.5%
ValueCountFrequency (%)
24.72 1
0.7%
29.84 1
0.7%
30.96 1
0.7%
31.12 1
0.7%
34.96 1
0.7%
37.2 1
0.7%
40.8 1
0.7%
41.6 1
0.7%
41.76 1
0.7%
42.4 1
0.7%
ValueCountFrequency (%)
73.92 1
0.7%
73.28 1
0.7%
72.48 1
0.7%
72.4 2
1.3%
70.64 1
0.7%
70.24 1
0.7%
69.68 1
0.7%
69.52 1
0.7%
69.44 1
0.7%
69.12 1
0.7%

id_bio_y
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)2.4%
Missing26
Missing (%)17.1%
Memory size1.3 KiB
14618
47 
13171
42 
13172
37 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters630
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13171
2nd row13171
3rd row14618
4th row14618
5th row14618

Common Values

ValueCountFrequency (%)
14618 47
30.9%
13171 42
27.6%
13172 37
24.3%
(Missing) 26
17.1%

Length

2024-10-13T17:46:56.668331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:46:56.718538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14618 47
37.3%
13171 42
33.3%
13172 37
29.4%

Most occurring characters

ValueCountFrequency (%)
1 294
46.7%
3 79
 
12.5%
7 79
 
12.5%
4 47
 
7.5%
6 47
 
7.5%
8 47
 
7.5%
2 37
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 294
46.7%
3 79
 
12.5%
7 79
 
12.5%
4 47
 
7.5%
6 47
 
7.5%
8 47
 
7.5%
2 37
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 294
46.7%
3 79
 
12.5%
7 79
 
12.5%
4 47
 
7.5%
6 47
 
7.5%
8 47
 
7.5%
2 37
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 294
46.7%
3 79
 
12.5%
7 79
 
12.5%
4 47
 
7.5%
6 47
 
7.5%
8 47
 
7.5%
2 37
 
5.9%

f_h_inicio_ino
Date

MISSING 

Distinct70
Distinct (%)57.4%
Missing30
Missing (%)19.7%
Memory size1.3 KiB
Minimum2023-03-27 05:22:00+00:00
Maximum2024-03-22 06:24:00+00:00
2024-10-13T17:46:56.780991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:56.856711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

f_h_fin_ino
Date

MISSING 

Distinct72
Distinct (%)59.0%
Missing30
Missing (%)19.7%
Memory size1.3 KiB
Minimum2023-03-28 05:29:00+00:00
Maximum2024-03-23 07:28:00+00:00
2024-10-13T17:46:56.932267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:57.009377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vol_cultivo_ino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)27.0%
Missing30
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean149.68131
Minimum79.2
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:57.080652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum79.2
5-th percentile80.02
Q1160
median160.08
Q3166.4
95-th percentile168.4
Maximum176
Range96.8
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation31.20426
Coefficient of variation (CV)0.20847132
Kurtosis1.0313651
Mean149.68131
Median Absolute Deviation (MAD)5.52
Skewness-1.7064801
Sum18261.12
Variance973.70584
MonotonicityNot monotonic
2024-10-13T17:46:57.148337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
160 36
23.7%
165.6 11
 
7.2%
168 7
 
4.6%
166.4 7
 
4.6%
80 6
 
3.9%
167.2 6
 
3.9%
84 4
 
2.6%
163.2 4
 
2.6%
164 3
 
2.0%
168.4 2
 
1.3%
Other values (23) 36
23.7%
(Missing) 30
19.7%
ValueCountFrequency (%)
79.2 1
 
0.7%
80 6
3.9%
80.4 1
 
0.7%
80.8 1
 
0.7%
81.6 1
 
0.7%
82.4 1
 
0.7%
82.64 1
 
0.7%
82.8 1
 
0.7%
83.2 2
 
1.3%
84 4
2.6%
ValueCountFrequency (%)
176 2
 
1.3%
168.8 2
 
1.3%
168.72 2
 
1.3%
168.4 2
 
1.3%
168.32 2
 
1.3%
168 7
4.6%
167.76 2
 
1.3%
167.2 6
3.9%
167.12 2
 
1.3%
166.4 7
4.6%

turb_inicio_cultivo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)40.0%
Missing32
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean15.337333
Minimum11.2
Maximum21.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:57.216928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile12.72
Q114.24
median15.08
Q316.3
95-th percentile18.248
Maximum21.52
Range10.32
Interquartile range (IQR)2.06

Descriptive statistics

Standard deviation1.753178
Coefficient of variation (CV)0.11430788
Kurtosis1.5062348
Mean15.337333
Median Absolute Deviation (MAD)1
Skewness0.72865638
Sum1840.48
Variance3.0736332
MonotonicityNot monotonic
2024-10-13T17:46:57.289890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
14.16 5
 
3.3%
15.44 5
 
3.3%
16.8 4
 
2.6%
13.2 4
 
2.6%
16.64 4
 
2.6%
18.24 4
 
2.6%
14.72 4
 
2.6%
15.92 4
 
2.6%
16.16 4
 
2.6%
16.96 4
 
2.6%
Other values (38) 78
51.3%
(Missing) 32
21.1%
ValueCountFrequency (%)
11.2 1
 
0.7%
12.16 2
1.3%
12.32 2
1.3%
12.72 2
1.3%
12.88 2
1.3%
13.2 4
2.6%
13.28 2
1.3%
13.68 1
 
0.7%
13.84 2
1.3%
13.92 2
1.3%
ValueCountFrequency (%)
21.52 2
1.3%
18.88 2
1.3%
18.4 2
1.3%
18.24 4
2.6%
18 1
 
0.7%
17.6 2
1.3%
17.28 1
 
0.7%
16.96 4
2.6%
16.88 2
1.3%
16.8 4
2.6%

turb_fin_cultivo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)45.2%
Missing26
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean26.119365
Minimum17.68
Maximum32.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:57.356579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17.68
5-th percentile21.1
Q124.24
median26.12
Q328.06
95-th percentile31.68
Maximum32.88
Range15.2
Interquartile range (IQR)3.82

Descriptive statistics

Standard deviation3.1340687
Coefficient of variation (CV)0.11999023
Kurtosis0.34239185
Mean26.119365
Median Absolute Deviation (MAD)1.88
Skewness-0.12075199
Sum3291.04
Variance9.8223868
MonotonicityNot monotonic
2024-10-13T17:46:57.544617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.56 7
 
4.6%
27.6 6
 
3.9%
25.52 5
 
3.3%
23.36 4
 
2.6%
24.48 4
 
2.6%
27.04 4
 
2.6%
28.48 4
 
2.6%
24.24 4
 
2.6%
23.52 4
 
2.6%
26.24 3
 
2.0%
Other values (47) 81
53.3%
(Missing) 26
 
17.1%
ValueCountFrequency (%)
17.68 2
1.3%
18.08 1
 
0.7%
19.36 1
 
0.7%
19.68 1
 
0.7%
20.88 2
1.3%
21.76 1
 
0.7%
21.84 1
 
0.7%
21.92 2
1.3%
22.32 3
2.0%
22.8 1
 
0.7%
ValueCountFrequency (%)
32.88 2
1.3%
32.8 2
1.3%
32.64 1
0.7%
32.32 1
0.7%
31.68 2
1.3%
30.8 1
0.7%
30.64 2
1.3%
30.48 1
0.7%
30 2
1.3%
29.84 2
1.3%

viab_fin_cultivo_ino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)43.7%
Missing26
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean99303867
Minimum37040000
Maximum2.52 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:57.617847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum37040000
5-th percentile77380000
Q185800000
median95600000
Q31.062 × 108
95-th percentile1.344 × 108
Maximum2.52 × 108
Range2.1496 × 108
Interquartile range (IQR)20400000

Descriptive statistics

Standard deviation24016427
Coefficient of variation (CV)0.24184785
Kurtosis12.907689
Mean99303867
Median Absolute Deviation (MAD)10000000
Skewness2.2594131
Sum1.2512287 × 1010
Variance5.7678874 × 1014
MonotonicityNot monotonic
2024-10-13T17:46:57.687340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86400000 7
 
4.6%
134400000 6
 
3.9%
91200000 6
 
3.9%
104000000 6
 
3.9%
104800000 5
 
3.3%
101600000 4
 
2.6%
112000000 4
 
2.6%
83200000 4
 
2.6%
82400000 4
 
2.6%
88000000 4
 
2.6%
Other values (45) 76
50.0%
(Missing) 26
 
17.1%
ValueCountFrequency (%)
37040000 2
1.3%
56800000 1
 
0.7%
66640000 1
 
0.7%
75767200 1
 
0.7%
76800000 2
1.3%
79120000 1
 
0.7%
79440000 2
1.3%
79760000 1
 
0.7%
79840000 2
1.3%
81600000 3
2.0%
ValueCountFrequency (%)
252000000 1
 
0.7%
167200000 1
 
0.7%
154400000 1
 
0.7%
144800000 1
 
0.7%
143200000 1
 
0.7%
134400000 6
3.9%
131200000 2
 
1.3%
130400000 1
 
0.7%
127000000 1
 
0.7%
122400000 1
 
0.7%

turb_diff_ino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)55.8%
Missing32
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean10.626667
Minimum1.84
Maximum18.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:57.753334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.84
5-th percentile5.504
Q19.34
median10.56
Q312.4
95-th percentile15.12
Maximum18.48
Range16.64
Interquartile range (IQR)3.06

Descriptive statistics

Standard deviation2.9827485
Coefficient of variation (CV)0.28068524
Kurtosis0.96279761
Mean10.626667
Median Absolute Deviation (MAD)1.6
Skewness-0.12108399
Sum1275.2
Variance8.8967888
MonotonicityNot monotonic
2024-10-13T17:46:57.824707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6 4
 
2.6%
9.36 4
 
2.6%
8.32 4
 
2.6%
13.2 3
 
2.0%
6.8 3
 
2.0%
11.2 3
 
2.0%
5.52 2
 
1.3%
13.6 2
 
1.3%
9.84 2
 
1.3%
13.68 2
 
1.3%
Other values (57) 91
59.9%
(Missing) 32
 
21.1%
ValueCountFrequency (%)
1.84 2
1.3%
4.64 2
1.3%
5.2 1
 
0.7%
5.2 1
 
0.7%
5.52 2
1.3%
5.68 1
 
0.7%
6.64 1
 
0.7%
6.8 3
2.0%
6.88 1
 
0.7%
7.28 2
1.3%
ValueCountFrequency (%)
18.48 2
1.3%
18.4 1
0.7%
16.96 1
0.7%
15.2 1
0.7%
15.12 2
1.3%
14.48 2
1.3%
14.48 2
1.3%
14 2
1.3%
13.92 1
0.7%
13.68 1
0.7%

dur_ino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)30.3%
Missing30
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean87690
Minimum79500
Maximum108600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:57.893352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum79500
5-th percentile82200
Q185200
median86400
Q389400
95-th percentile100455
Maximum108600
Range29100
Interquartile range (IQR)4200

Descriptive statistics

Standard deviation5046.591
Coefficient of variation (CV)0.05755036
Kurtosis5.4555043
Mean87690
Median Absolute Deviation (MAD)1500
Skewness2.062718
Sum10698180
Variance25468081
MonotonicityNot monotonic
2024-10-13T17:46:57.960881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
86400 25
16.4%
85200 7
 
4.6%
90000 5
 
3.3%
87000 5
 
3.3%
86700 5
 
3.3%
88200 5
 
3.3%
83700 4
 
2.6%
89400 4
 
2.6%
87300 4
 
2.6%
86100 4
 
2.6%
Other values (27) 54
35.5%
(Missing) 30
19.7%
ValueCountFrequency (%)
79500 2
1.3%
80100 2
1.3%
81900 2
1.3%
82200 2
1.3%
82500 3
2.0%
82860 2
1.3%
83400 1
 
0.7%
83700 4
2.6%
84420 2
1.3%
84600 3
2.0%
ValueCountFrequency (%)
108600 1
 
0.7%
105600 1
 
0.7%
105300 2
1.3%
102600 1
 
0.7%
101700 1
 
0.7%
100800 1
 
0.7%
93900 2
1.3%
93600 2
1.3%
92400 2
1.3%
91800 3
2.0%

Interactions

2024-10-13T17:46:52.566234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:27.154353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:31.679115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.153698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.423748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:35.525951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.854083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.091211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.249596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:40.538084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:41.811913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.057002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.356033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:45.541091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.804265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.877571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.055417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.283110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.353139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:52.830866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:27.741815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.124030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.599943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.724832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:35.961528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:37.171291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.404129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.555320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:40.835151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.127697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.395413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.681707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:45.977821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.068805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.256628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.328585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.553167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.608064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:52.861371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:27.991328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.212130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.673032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.794052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.041847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:37.243841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.477934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.745475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:40.910669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.204309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.470442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.756260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.050503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.099435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.286002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.361886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.581982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.638743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:52.906043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:28.200838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.272041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.711566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.831808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.086112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:37.282479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.519703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.787956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:40.952467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.247087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.510978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.796402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.092222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.139092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.323466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.401572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.619958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.677579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:52.968997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:28.399502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.330462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.750091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.868016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.130819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:37.321785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.560905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.834311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:40.999442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.288858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.551883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.837646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.131552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.184638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.368223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.442674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.660682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.722949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:53.017878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:28.608430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.397075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.796161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.911176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.178272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:37.367458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.608564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.881993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:41.047514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.341294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.716832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.884705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.177866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.231529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.416147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.492056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.708189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.773116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:53.062444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:28.936676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.459164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.837241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.949726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.224827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:37.406239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.650543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.926691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:41.090964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.393807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:43.757839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.926044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.221443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.273125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.458407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.542884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.751671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.816651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:53.108970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:29.147547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:32.524852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.882801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-10-13T17:46:46.711153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.784376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:48.961170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.185308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.260207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:52.471792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:53.642035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:31.504030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:33.115078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:34.378839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:35.479248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:36.803046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:38.045583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:39.201765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:40.489463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:41.763401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:42.996397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:44.308330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:45.490115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:46.758209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:47.830710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:49.008390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:50.235113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:51.307861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:52.517884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-13T17:46:58.021540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
centr_1_turb_cfcentr_2_turb_cfdur_cfdur_inoid_bio_xid_bio_yid_centrlotelote_parental_cforden_encadenado_cfproducto_1_cfproducto_2_cfturb_diff_inoturb_fin_cultivoturb_fin_cultivo_cfturb_inicio_cultivoturb_inicio_cultivo_cfturbidez_diff_cfviab_fin_cultivo_inoviab_final_cultivo_cfvol_cultivo_inovol_ino_util_cf
centr_1_turb_cf1.0000.356-0.1990.036-0.2020.0000.0000.393-0.0890.0000.037-0.0190.1910.2680.4070.1220.4740.280-0.036-0.004-0.097-0.027
centr_2_turb_cf0.3561.000-0.3020.012-0.1760.0990.0170.228-0.1370.0000.1940.4280.3920.2970.506-0.1350.0890.467-0.0250.141-0.062-0.073
dur_cf-0.199-0.3021.000-0.2550.1870.1840.000-0.334-0.4290.587-0.135-0.267-0.213-0.324-0.461-0.166-0.370-0.328-0.027-0.201-0.0550.167
dur_ino0.0360.012-0.2551.000-0.0440.2720.1090.334NaN1.000-0.277-0.0770.1930.138-0.147-0.0450.011-0.181-0.108-0.3200.151-0.075
id_bio_x-0.202-0.1760.187-0.0441.0000.0000.220-0.178-0.2470.064-0.087-0.193-0.004-0.056-0.193-0.077-0.062-0.158-0.090-0.1170.0710.029
id_bio_y0.0000.0990.1840.2720.0001.0000.0001.0001.0001.0000.1010.0000.2320.1930.0000.2650.0840.0940.0420.0590.1830.000
id_centr0.0000.0170.0000.1090.2200.0001.0001.0000.0000.0000.0440.1260.0000.0000.0000.0000.0000.0000.0000.2240.0000.000
lote0.3930.228-0.3340.334-0.1781.0001.0001.0000.9981.000-0.2200.0360.048-0.1240.194-0.2470.1950.133-0.079-0.082-0.081-0.173
lote_parental_cf-0.089-0.137-0.429NaN-0.2471.0000.0000.9981.0000.748-0.102-0.153NaNNaN0.436NaN-0.5300.467NaN-0.379NaN0.521
orden_encadenado_cf0.0000.0000.5871.0000.0641.0000.0001.0000.7481.0000.0000.1831.0001.0000.0001.0000.4020.0001.0000.0001.0000.270
producto_1_cf0.0370.194-0.135-0.277-0.0870.1010.044-0.220-0.1020.0001.0000.5130.2270.3160.4320.1140.0630.4330.1180.128-0.092-0.105
producto_2_cf-0.0190.428-0.267-0.077-0.1930.0000.1260.036-0.1530.1830.5131.0000.1990.1480.498-0.1290.0660.4960.0110.079-0.095-0.227
turb_diff_ino0.1910.392-0.2130.193-0.0040.2320.0000.048NaN1.0000.2270.1991.0000.8230.328-0.101-0.0140.3260.030-0.106-0.099-0.124
turb_fin_cultivo0.2680.297-0.3240.138-0.0560.1930.000-0.124NaN1.0000.3160.1480.8231.0000.4120.4190.1690.3580.077-0.004-0.125-0.095
turb_fin_cultivo_cf0.4070.506-0.461-0.147-0.1930.0000.0000.1940.4360.0000.4320.4980.3280.4121.0000.0650.2190.9440.1080.286-0.183-0.294
turb_inicio_cultivo0.122-0.135-0.166-0.045-0.0770.2650.000-0.247NaN1.0000.114-0.129-0.1010.4190.0651.0000.330-0.0240.0520.041-0.1300.060
turb_inicio_cultivo_cf0.4740.089-0.3700.011-0.0620.0840.0000.195-0.5300.4020.0630.066-0.0140.1690.2190.3301.000-0.0400.029-0.0560.078-0.103
turbidez_diff_cf0.2800.467-0.328-0.181-0.1580.0940.0000.1330.4670.0000.4330.4960.3260.3580.944-0.024-0.0401.0000.1160.276-0.202-0.236
viab_fin_cultivo_ino-0.036-0.025-0.027-0.108-0.0900.0420.000-0.079NaN1.0000.1180.0110.0300.0770.1080.0520.0290.1161.0000.1190.1500.005
viab_final_cultivo_cf-0.0040.141-0.201-0.320-0.1170.0590.224-0.082-0.3790.0000.1280.079-0.106-0.0040.2860.041-0.0560.2760.1191.000-0.219-0.024
vol_cultivo_ino-0.097-0.062-0.0550.1510.0710.1830.000-0.081NaN1.000-0.092-0.095-0.099-0.125-0.183-0.1300.078-0.2020.150-0.2191.0000.323
vol_ino_util_cf-0.027-0.0730.167-0.0750.0290.0000.000-0.1730.5210.270-0.105-0.227-0.124-0.095-0.2940.060-0.103-0.2360.005-0.0240.3231.000

Missing values

2024-10-13T17:46:53.723778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-13T17:46:53.898956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-13T17:46:54.157059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loteorden_encadenado_cflote_parental_cfid_bio_xf_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cfid_bio_yf_h_inicio_inof_h_fin_inovol_cultivo_inoturb_inicio_cultivoturb_fin_cultivoviab_fin_cultivo_inoturb_diff_inodur_ino
0230191nan146152023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0082.417.2891.20184000000.017825NaNNaN1747.9206.00169200.073.9213171NaTNaTNaNNaN32.80134400000.0NaNNaN
1230201nan146162023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0080.418.8091.20181600000.014246NaNNaN1676.1606.56169200.072.4013171NaTNaTNaNNaN32.80134400000.0NaNNaN
2230211nan131702023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0066.416.1686.40248000000.017825NaNNaN1928.4968.08169200.070.2414618NaTNaTNaNNaN27.84115200000.0NaNNaN
3230221nan146142023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0085.618.4883.20229600000.012912NaNNaN1782.8005.92169200.064.7214618NaTNaTNaNNaN27.84115200000.0NaNNaN
4230231nan146152023-03-28 05:27:00+00:002023-03-30 08:00:00+00:0077.617.1274.40132800000.01782526.5620.881861.8402.96181980.057.28146182023-03-27 05:22:00+00:002023-03-28 05:29:00+00:00155.218.2431.68106400000.013.4486820.0
5230241nan146162023-03-28 05:24:00+00:002023-03-30 05:23:00+00:0076.016.5680.80199200000.01424624.5610.402161.1202.80172740.064.24146182023-03-27 05:22:00+00:002023-03-28 05:29:00+00:00155.218.2431.68106400000.013.4486820.0
6230251nan131702023-03-29 05:09:00+00:002023-03-31 05:29:00+00:0077.217.7687.20199200000.01782530.6429.362044.7204.48174000.069.44131722023-03-28 05:42:00+00:002023-03-29 04:43:00+00:00157.616.9627.6084800000.010.6482860.0
7230261nan146142023-03-29 05:29:00+00:002023-03-31 05:38:00+00:0078.818.2481.20206400000.01424626.4811.602263.2003.44173340.062.96131722023-03-28 05:42:00+00:002023-03-29 04:43:00+00:00157.616.9627.6084800000.010.6482860.0
8230271nan146152023-04-04 08:32:00+00:002023-04-06 10:30:00+00:0083.216.8868.08195200000.01424626.249.841407.6804.08179880.051.20131722023-04-03 11:30:00+00:002023-04-04 09:35:00+00:00168.018.8823.52104800000.04.6479500.0
9230281nan146162023-04-04 08:34:00+00:002023-04-06 10:32:00+00:0083.618.5667.20176000000.01782527.2812.161373.2004.72179880.048.64131722023-04-03 11:30:00+00:002023-04-04 09:35:00+00:00168.018.8823.52104800000.04.6479500.0
loteorden_encadenado_cflote_parental_cfid_bio_xf_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cfid_bio_yf_h_inicio_inof_h_fin_inovol_cultivo_inoturb_inicio_cultivoturb_fin_cultivoviab_fin_cultivo_inoturb_diff_inodur_ino
14224046224041.0131692024-03-11 12:10:00+00:002024-03-13 09:50:00+00:0080.0019.9288.00156800000.01291232.7228.321783.847.84164400.068.08NaNNaTNaTNaNNaNNaNNaNNaNNaN
143240431nan146142024-03-12 06:25:00+00:002024-03-14 07:25:00+00:0080.0019.4469.68132800000.01424630.3216.161254.564.72176400.050.24131722024-03-11 06:15:00+00:002024-03-12 06:25:00+00:00165.6013.8423.20109600000.09.3687000.0
144240451nan146162024-03-12 06:25:00+00:002024-03-14 08:15:00+00:0080.0017.5272.48139200000.01291227.8417.761573.525.76179400.054.96131722024-03-11 06:15:00+00:002024-03-12 06:25:00+00:00165.6013.8423.20109600000.09.3687000.0
145240441nan131702024-03-16 08:20:00+00:002024-03-18 07:01:00+00:0083.6019.2877.52160800000.01424630.7220.681528.725.44168060.058.24131712024-03-15 06:20:00+00:002024-03-16 08:00:00+00:00167.2014.8025.28104800000.010.4892400.0
14624047224044.0131702024-03-18 12:00:00+00:002024-03-20 06:00:00+00:0080.0018.2486.40223200000.01424628.1626.761794.326.64151200.068.16NaNNaTNaTNaNNaNNaNNaNNaNNaN
147240491nan146172024-03-16 08:22:00+00:002024-03-18 07:23:00+00:0083.6018.8872.64164800000.01291230.5617.001342.804.88169260.053.76131712024-03-15 06:20:00+00:002024-03-16 08:00:00+00:00167.2014.8025.28104800000.010.4892400.0
148240501nan146142024-03-23 07:57:00+00:002024-03-25 07:28:00+00:0084.1617.7667.60152000000.0637929.4426.641422.803.68171060.049.84146182024-03-22 06:24:00+00:002024-03-23 07:28:00+00:00168.3214.7225.92102400000.011.2090240.0
149240511nan131692024-03-23 07:57:00+00:002024-03-25 07:33:00+00:0084.1617.7680.80160800000.01291233.4419.321486.565.52171360.063.04146182024-03-22 06:24:00+00:002024-03-23 07:28:00+00:00168.3214.7225.92102400000.011.2090240.0
15024052224050.0146142024-03-25 12:28:00+00:002024-03-27 07:51:00+00:0086.4017.2869.04148000000.01424623.6818.201857.286.00156180.051.76NaNNaTNaTNaNNaNNaNNaNNaNNaN
15124053224051.0131692024-03-25 11:27:00+00:002024-03-27 07:27:00+00:0087.2016.7279.36148000000.01291226.5619.161784.087.20158400.062.64NaNNaTNaTNaNNaNNaNNaNNaNNaN